1. Field of the Invention
The present invention relates to systems and methods for estimating vital signs from passive thermal video, and more specifically to measuring pulse and respiratory rate from passive thermal video using subject alignment, signal enhancement and harmonic analysis.
2. Background of the Invention
Vital signs are important physiological parameters for health monitoring and emotion recognition. However, wired detection of these parameters limits their feasibility in numerous applications. If these parameters can be detected wirelessly and safely, they can be used more flexibly in many applications, such as airport health screening, elder care, and workplace preventative care.
Sensors for the measurement of the human heart beat and breath rate include the Piezo Pulse Transducer, Piezo Respiratory Transducer, and ECG electrodes. All these measurement sensors have to be attached to a human body and wired to preamps and processing instruments. These measurement approaches place severe restrictions on applications using heart beat and breath rate parameters.
Recently, non-contact measurement methods have been developed. Sun and Garbey et al. did experiments on heart beat measurement with passive thermal video. N. Sun, M. Garbey, A. Merla, and I. Pavlidis; Estimation of blood flow speed and vessel location from thermal video; Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 1, pp 356-363; and N. Sun, M. Garbey, A. Merla, and I. Pavlidis; Imaging the cardiovascular pulse; Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2, pp 416-421; and Marc Garbey, Nanfei Sun, Arcangelo Merla, and Ioannis Pavlidis; Contact-Free measurement of cardiac Pulse Based on the Analysis of thermal Imagery, IEEE Transactions on Biomedical Engineering; August 2007, vol. 54, issue 8, pp. 1418-1426. These systems are limited for two primary reasons. First, they need artificial markers. Since the artificial markers cannot be at the same location of the detection region (otherwise, the marker will cover the detection region and totally block the signal) and human body is not rigid, the pixel alignment in detection regions based on markers' motion cannot be very accurate for pixel alignment. Inaccurate pixel alignment will cause significant problems for later heartbeat signal detection. Second, Sun et. al's approach of finding the central line of a blood vessel is very sensitive to noise. Wrong detection of the blood vessel central line will lead to pixel misalignment and may ruin the signal detection.
Chekmenev et al. described a Superficial Temporal Artery (STA) measurement model based on arterial wall volumetric change corresponding to blood pressure modulation. S. Y. Chekmenev, A. A. Farag, E. A. Essock; Multiresolution approach for non-contact measurements of arterial pulse using thermal imaging; CVPR 2006 Workshop, and S. Y. Chekmenev, A. A. Farag, E. A. Essock. Thermal Imaging of the Superficial Temporal Artery: An Arterial Pulse Recovery Model. OTCBVS 2007.
In other systems, it is assumed that the measured subject is perfectly static. N. Sun et. al, vol. 1, pp 356-363. That is not true, however, for many pulse rate measurement tasks. Some systems use the hottest pixels to track the center of a blood vessel. N. Sun et. al, vol. 2, pp 416-421; and Marc Garbey et. al, pp. 1418-1426. Since the derivatives around the maximum values are relatively small, tracking based on those hottest spots tends to be noisy. Another system tracks subjects based on foil markers placed on the subject that are not available under most measurement scenarios. See Michael Wübbenhorst; Thermal Wave Techniques; http://www.polymers.tudelft.nl/wubweb/thermalwaves.html.
These earlier approaches involve strong assumptions on subject motion, type and location of blood vessels, etc. Their algorithms also have many parameters that are difficult to incorporate into an automatic detection system. Moreover, the signal detection and denoising model used in these systems are very preliminary, as discussed above. Thus, the existing technology fails to provide an automatic detection system and method that accurately estimates vital sign information using passive thermal video.